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Deploying AI Models with NVIDIA Triton Inference Server

Master scalable, high-performance AI model deployment using NVIDIA Triton Inference Server for real-time, multi-framework inferencing.
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Course Duration: 10 Hours
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Deploying AI Models with NVIDIA Triton Inference Server – Online Course
 
Deploying AI Models with NVIDIA Triton Inference Server is a specialized, industry-oriented course designed to help you master the art and science of deploying AI and deep learning models in production environments at scale. With the growing complexity and diversity of AI models, enterprises need a powerful and flexible deployment engine—NVIDIA Triton delivers just that.
 
Triton Inference Server (formerly TensorRT Inference Server) supports model serving across frameworks like TensorFlow, PyTorch, ONNX Runtime, and TensorRT, with built-in features for GPU acceleration, batching, dynamic model loading, and concurrent inferencing.
 
But what exactly is NVIDIA Triton, and how is it used?
 
Triton is an open-source inference serving software developed by NVIDIA to simplify the deployment and execution of AI models. It provides a standardized interface, multi-framework compatibility, and optimized inferencing pipelines for real-time, batch, and streaming data use cases. Whether you're deploying a computer vision model, an LLM, or an ensemble model, Triton enables you to manage inference workloads reliably, efficiently, and at scale.
 
How to use this course:
  • Begin with fundamentals of model deployment and MLOps principles.
  • Learn Triton’s architecture, configuration files, and supported backends.
  • Follow practical labs to deploy PyTorch, TensorFlow, ONNX, and ensemble models.
  • Explore performance optimization with model batching, scheduling, and GPU memory management.
  • Build end-to-end systems using REST/gRPC APIs, Kubernetes, and Prometheus monitoring.
This course is essential for MLOps engineers, data scientists, system architects, and developers looking to productionize their AI models with enterprise-grade performance and control.

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand the fundamentals of AI model deployment and inference.
  2. Set up and configure NVIDIA Triton Inference Server in cloud, local, or containerized environments.
  3. Serve models from TensorFlow, PyTorch, ONNX, and TensorRT backends.
  4. Use Triton’s model repository structure and configuration system effectively.
  5. Implement batching, dynamic shape support, and concurrent model execution.
  6. Leverage Triton’s HTTP/gRPC APIs for scalable integration with applications.
  7. Monitor and profile model performance using Triton Metrics and Prometheus/Grafana.
  8. Deploy ensemble models and multi-model pipelines for advanced workflows.
  9. Integrate Triton into Kubernetes for dynamic scaling and orchestration.
  10. Build a complete AI inference system from model loading to client integration.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to AI Model Deployment
  • Inference vs. training
  • Key deployment challenges
  • Serving architectures and design patterns
Module 2: Introduction to NVIDIA Triton
  • What is Triton Inference Server?
  • Use cases and architecture overview
  • Supported backends and formats
Module 3: Setting Up Triton
  • Installation using Docker and NGC
  • Understanding model repository layout
  • Configuring Triton with config.pbtxt
Module 4: Serving Models with Triton
  • Deploying PyTorch, TensorFlow, ONNX models
  • Using TensorRT optimized models
  • Dynamic shapes and input batching
Module 5: Using Triton Inference APIs
  • REST and gRPC API overview
  • Building client-side applications
  • Using the Python Triton client
Module 6: Performance Optimization
  • Auto-batching and scheduling
  • Model prioritization and instance groups
  • CPU/GPU memory optimization
Module 7: Advanced Deployment Features
  • Model versioning
  • Ensemble model configuration
  • Custom backends and plugins
Module 8: Observability and Monitoring
  • Logging and tracing
  • Prometheus integration for metrics
  • Grafana dashboards for inference insights
Module 9: Scaling with Kubernetes and Helm
  • Running Triton on Kubernetes
  • Multi-model management
  • Load balancing and autoscaling
Module 10: Capstone Project
 
  • Build and deploy an AI-powered REST service
  • Monitor inference latency and throughput
  • Package and scale with Kubernetes
Certification Back to Top
Upon successful completion of the course, learners will receive a Certificate of Completion from Uplatz, validating their expertise in deploying and managing AI models using NVIDIA Triton Inference Server. The certification showcases your readiness to contribute to production AI pipelines and MLOps infrastructures in enterprise settings.
 
The certificate demonstrates mastery in multi-framework model serving, performance tuning, API integration, and system monitoring—skills highly valued in roles such as MLOps Engineer, AI Infrastructure Architect, and Deep Learning Deployment Specialist. Add this certificate to your LinkedIn or resume to prove your capability in taking AI models from the lab to real-time production.
Career & Jobs Back to Top
As AI adoption accelerates, the need for skilled professionals in model deployment and serving is skyrocketing. Tools like Triton Inference Server are critical in bridging the gap between AI research and enterprise operations.
 
Career opportunities include:
  • MLOps Engineer
  • AI Systems Engineer
  • Deep Learning Deployment Specialist
  • AI Infrastructure Architect
  • DevOps for AI/ML
  • Cloud ML Engineer
Industries such as autonomous vehicles, healthcare, finance, defense, and retail are deploying AI models in production and require experts to manage throughput, latency, and reliability at scale. With NVIDIA Triton skills, you’ll be equipped to support high-performance, multi-model serving pipelines in modern AI infrastructures.
 
Whether you're deploying computer vision models on edge devices or integrating LLMs into chatbots and search, this course prepares you to deploy, monitor, and optimize models efficiently.
Interview Questions Back to Top
1. What is the purpose of NVIDIA Triton Inference Server?
Triton simplifies and standardizes the deployment of AI models by supporting multiple frameworks and optimizing inference using batching and GPU acceleration.
 
2. What types of models can Triton serve?
It supports TensorFlow, PyTorch, ONNX, TensorRT, and custom backends—allowing flexibility across deep learning ecosystems.
 
3. What is a model repository in Triton?
It’s a structured folder where models are stored along with their versions and configuration files (config.pbtxt) to manage deployments.
 
4. How does Triton handle concurrent model execution?
Triton uses instance groups and scheduling policies to serve multiple models or model versions simultaneously across hardware resources.
 
5. What are the benefits of batching in Triton?
Batching improves throughput by grouping multiple inference requests into a single call, reducing processing overhead and increasing GPU utilization.
 
6. How does Triton expose APIs for inference?
It provides both REST and gRPC endpoints, allowing integration with any application stack using simple HTTP or protobuf interfaces.
 
7. What is an ensemble model in Triton?
It’s a pipeline of multiple models chained together to perform sequential inference, useful for workflows like preprocessing → prediction → postprocessing.
 
8. How is performance monitored in Triton?
Triton exposes Prometheus-compatible metrics like request count, latency, GPU utilization, and more, which can be visualized using Grafana.
 
9. How can Triton be deployed at scale?
Using container orchestration platforms like Kubernetes, you can scale Triton instances, balance load, and manage multiple model versions dynamically.
 
10. What is the role of TensorRT in Triton deployment?
TensorRT optimizes models for GPU inference by reducing precision (e.g., FP32 to INT8) and enabling faster execution with minimal latency.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

Q3. How long is the course access?
A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

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A4. Video courses cannot be downloaded, but you have lifetime access to any video course you purchase on our website. You will be able to play the videos on our our website and mobile app.

Q5. Do you take exam? Do I need to pass exam? How to book exam?
A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

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A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php

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A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

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Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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Q20. How do I access my course after payment?
A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.
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BUY THIS COURSE (USD 17 USD 41)